Abstract
Oreochromis niloticus or tilapia is the second major freshwater aquaculture bred after catfish in Malaysia. By understanding the feeding behaviour, fish farmers will able to identify the best feeding routine. In the present investigation, photoelectric sensors are used to identify the movement, speed and position of the fish. The signals acquired from the sensors are converted into binary data. The hunger behaviour classes are determined through k-means clustering algorithm, i.e., satiated and unsatiated. The Logistic Regression (LR) classifier was employed to classify the aforesaid hunger state. The model was trained by means of 5-fold cross-validation technique. It was shown that the LR model is able to yield a classification accuracy for tested data during the day at three different time windows (4 h each) is 100%, 88.7% and 100%, respectively, whilst the for-night data it was shown to demonstrate 100% classification accuracy.
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References
Benhaïm, D., Akian, D.D., Ramos, M., Ferrari, S., Yao, K., Bégout, M.L.: Self-feeding behaviour and personality traits in tilapia: a comparative study between Oreochromis niloticus and Sarotherodon melanotheron. Appl. Anim. Behav. Sci. 187, 85–92 (2017)
Hansen, M.J., Schaerf, T.M., Ward, A.J.W.: The effect of hunger on the exploratory behaviour of shoals of mosquitofish Gambusia holbrooki. Behaviour 152, 1659–1677 (2015)
Sanchez-Vázquez, F.J., Madrid, J.A., Zamora, S.: Circadian rhythms of feeding activity in sea bass, Dicentrarchus labrax L.: dual phasing capacity of diel demand-feeding pattern. J. Biol. Rhythms 10, 256–266 (1995)
Taha, Z., et al.: The identification of hunger behaviour of Lates Calcarifer through the integration of image processing technique and support vector machine. In: IOP Conference of Series of Materials Science and Engineering, vol. 319, p. 012028 (2018)
Taha, Z., et al.: The classification of hunger behaviour of Lates Calcarifer through the integration of image processing technique and k-Nearest Neighbour learning algorithm. In: IOP Conference of Series of Materials Science and Engineering, vol. 342, p. 012017 (2018)
Taha, Z., et al.: The Identification of hunger behaviour of Lates Calcarifer using k-nearest neighbour (2018)
Siddiqui, S.A., et al.: Automatic fish species classification in underwater videos: exploiting pre-trained deep neural network models to compensate for limited labelled data. ICES J. Mar. Sci. 75, 374–389 (2018)
Muazu Musa, R., Taha, Z., Abdul Majeed, A.P.P., Abdullah, M.R.: Machine Learning in Sports: Identifying Potential Archers. SAST. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-2592-2
Acknowledgement
The final outcome of this research project and the successful of development of this useful system required a lot of guidance and assistance from my project supervisor. Meanwhile, I would like to express my gratitude to lab instructors and my friends for providing practically knowledge, skills and guidance when doing the mechanical work in lab. This work is partially support by Universiti Malaysia Pahang, Automotive Engineering Centre (AEC) research grant RDU1803131 entitled Development of Multi-vision guided obstacle Avoidance System for Ground Vehicle.
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Mohd Sojak, M.R., Mohd Razman, M.A., P. P. Abdul Majeed, A., Musa, R.M., Abdul Ghani, A.S., Iskandar, I. (2019). The Identification of Oreochromis niloticus Feeding Behaviour Through the Integration of Photoelectric Sensor and Logistic Regression Classifier. In: Kim, JH., Myung, H., Lee, SM. (eds) Robot Intelligence Technology and Applications. RiTA 2018. Communications in Computer and Information Science, vol 1015. Springer, Singapore. https://doi.org/10.1007/978-981-13-7780-8_18
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DOI: https://doi.org/10.1007/978-981-13-7780-8_18
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